Developing and evaluating a Bayesian weather generator for UK precipitation conditioned on discrete storm types

Abstract. Weather generators (WGs) are important tools for downscaling General Circulation Model (GCM) output for climate impact modelling. This study introduces a precipitation WG conditioned on a recent storm types dataset and outlines a methodology for evaluating WGs using proper scoring rules. The storm types are a set of discrete weather types that use atmospheric variables to categorise ERA5 grid cells into fronts, cyclones and thunderstorms or combinations of these. The WG is a Bayesian Generalised Linear Model (GLM) with vertical velocity and humidity as covariates, conditioned on the storm types, and trained on 6 hourly precipitation accumulations. This approach contrasts with previous WGs based on weather types, which use clustering methods such as k-means to generate weather types. A Bayesian model framework is used, instead of typical maximum likelihood approaches, and an informed prior choice is made on observable quantities using the prior predictive distribution. The WG is assessed using proper scoring rules and Diebold-Mariano (DM) significance tests. The use of a DM test to assess the statistical significance of average proper score differences is a key addition to typical WG evaluation approaches, as it helps model developers avoid changes that improve an average score by chance. Calibration is assessed using the probability integral transform histogram and by comparing draws from the posterior predictive distribution to observations. Compared to the same WG not conditioned on storm types the inclusion of storm types improved the average Continuous Ranked Probably Score (CRPS) by a statistically significant amount across the stations considered. When storm types are used as an alternative to continuous atmospheric variables, they provide 33 % of the improvement in average CRPS that the atmospheric variables do, averaged over the stations. To quantify the WG's ability to represent extremes the threshold weighted CRPS (twCRPS) is explored. For three different thresholds the twCRPS corroborates the results for the CRPS. The use of proper scoring rules in conjunction with a DM test is highlighted as a powerful tool for assessing WG skill.